def tagging(documents):
nDocs = len(documents)
documentsProcessed = []
unigram_tagger = []
try:
unigram_tagger = file_utils.load_object('tagger1','tagger', None)
except:
train_set = mac_morpho.tagged_sents() # Treinamento do tagger via dicionário mac_morpho
unigram_tagger = nltk.UnigramTagger(train_set) # Aplicação do tagger ao documento
file_utils.save_object(unigram_tagger, 'tagger1','tagger', None) # Salva os unigramas tagueados a serem dicionados aos documentos processados
for iDoc in range(0,nDocs): # Ordenação dos diferentes documentos a serem processados
documentsProcessed.append(unigram_tagger.tag(documents[iDoc])) # Adiciona à lista a partir da ordenação
return documentsProcessed
def get_random_predicted_samples(model, dataset, labels):
samples_amount = 5
correct_predictions = []
incorrect_predictions = []
num_samples, _ = dataset.shape
i = 0
while i < num_samples and \
(len(correct_predictions) < samples_amount or len(incorrect_predictions) < samples_amount):
feature_vector = np.array(dataset.iloc[i, :]).reshape(1, -1)
predicted_label = model.predict(feature_vector)[0]
correct_label = labels[i]
prediction = (i, predicted_label, correct_label)
if predicted_label == correct_label:
correct_predictions.append(prediction)
else:
incorrect_predictions.append(prediction)
i += 1
for k, (i, predicted_label,
correct_label) in enumerate(correct_predictions):
tweet_id = dataset.index[i]
tweet = load_object("data/tweets/" + str(tweet_id))
tweet_prediction = (tweet, predicted_label, correct_label)
correct_predictions[k] = tweet_prediction
for k, (i, predicted_label,
correct_label) in enumerate(incorrect_predictions):
tweet_id = dataset.index[i]
tweet = load_object("data/tweets/" + str(tweet_id))
tweet_prediction = (tweet, predicted_label, correct_label)
incorrect_predictions[k] = tweet_prediction
if len(correct_predictions) > 5:
correct_predictions = sample(correct_predictions, samples_amount)
if len(incorrect_predictions) > 5:
incorrect_predictions = sample(incorrect_predictions, samples_amount)
return correct_predictions, incorrect_predictions
def tagging(documents):
nDocs = len(documents)
# print nDocs
documentsProcessed = []
unigram_tagger = []
try:
unigram_tagger = file_utils.load_object('tagger1', 'tagger', None)
print unigram_tagger
except:
train_set = mac_morpho.tagged_sents()
#test_set = mac_morpho.tagged_sents()[10001:10010]
unigram_tagger = nltk.UnigramTagger(train_set)
file_utils.save_object(unigram_tagger, 'tagger1', 'tagger', None)
for iDoc in range(0, nDocs):
#tokens = documents[iDoc]
documentsProcessed.append(unigram_tagger.tag(documents[iDoc]))
return documentsProcessed
def tagging(documents):
nDocs = len(documents)
documentsProcessed = []
unigram_tagger = []
try:
unigram_tagger = file_utils.load_object('tagger1', 'tagger', None)
except:
train_set = mac_morpho.tagged_sents(
) # Treinamento do tagger via dicionário mac_morpho
unigram_tagger = nltk.UnigramTagger(
train_set) # Aplicação do tagger ao documento
file_utils.save_object(
unigram_tagger, 'tagger1', 'tagger', None
) # Salva os unigramas tagueados a serem dicionados aos documentos processados
for iDoc in range(
0,
nDocs): # Ordenação dos diferentes documentos a serem processados
documentsProcessed.append(unigram_tagger.tag(
documents[iDoc])) # Adiciona à lista a partir da ordenação
return documentsProcessed
def tagging(self, documents, savePath, language):
nDocs = len(documents)
documentsProcessed = []
unigram_tagger = []
from data_core.file_utils import FileUtils
file_utils = FileUtils(savePath)
try:
unigram_tagger = file_utils.load_object('tagger_' + language,
'tagger')
except:
if language == "pt":
train_set = mac_morpho.tagged_sents()
elif language == "en":
train_set = brown.tagged_sents(tagset='universal')
#print(train_set[0:1])
nSents = len(train_set)
train_set_lower = []
for iSent in range(0, nSents):
nWords = len(train_set[iSent])
words = []
for iWord in range(0, nWords):
words.append(
(self.text_lower_one([train_set[iSent][iWord][0]
])[0],
train_set[iSent][iWord][1]))
train_set_lower.append(words)
#print(train_set_lower[0:1])
#test_set = mac_morpho.tagged_sents()[10001:10010]
unigram_tagger = nltk.UnigramTagger(train_set_lower)
file_utils.save_object(unigram_tagger, 'tagger_' + language,
'tagger')
for iDoc in range(0, nDocs):
#tokens = documents[iDoc]
documentsProcessed.append(unigram_tagger.tag(documents[iDoc]))
return documentsProcessed
plt.plot([0, 1], [0, 1], color='red', lw=3, linestyle='--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.xlabel('False Positive Rate', fontsize=18)
plt.xticks(fontsize=14)
plt.ylabel('True Positive Rate', fontsize=18)
plt.yticks(fontsize=14)
plt.title('ROC Curve - ' + str(name) + '\nAccuracy: ' + str(accuracy) +
'\nROC AUC score: ' + str(roc_score),
fontsize=24)
plt.savefig(file, dpi="figure")
return confusion_mat
if __name__ == "__main__":
subreddits_dictionary = load_object(
'data/dictionaries/subreddits_dictionary_reduced')
involvement_dictionary = load_object(
'data/dictionaries/involvement_dictionary_reduced')
involvement = [value for value in involvement_dictionary.values()]
involvement = remove_extreme_values(involvement)
generate_distribution_plot(
involvement, 'User-subreddit involvement',
"plots/user_subreddit_involvement_distribution.png")
sus_network = nx.read_gpickle('data/networks/SuS')
sus_degrees = dict(nx.degree(sus_network))
sus_degrees = list(sus_degrees.values())
sus_network_reduced = reduce_graph_by_weight_threshold(
if len(incorrect_predictions) > 5:
incorrect_predictions = sample(incorrect_predictions, samples_amount)
return correct_predictions, incorrect_predictions
def print_tweet_samples(tweet_samples):
for tweet, predicted_class, correct_class in tweet_samples:
print("Tweet:", tweet)
print("Predicted class:", predicted_class)
print("Tweet class:", correct_class)
print()
if __name__ == "__main__":
top_comment_features = load_object(
"data/features/top/top_comment_features")
print("Top comment features:", top_comment_features)
top_query_features = load_object("data/features/top/top_query_features")
print("Top query features:", top_query_features)
top_support_deny_features = load_object(
"data/features/top/top_support_deny_features")
print("Top support vs deny features:", top_support_deny_features)
top_veracity_features = load_object(
"data/features/top/top_veracity_features")
print("Top veracity features:", top_veracity_features)
print()
comment_feature_labels = load_object(
'data/features/labels/task_a_comment_feature_labels')
query_feature_labels = load_object(
'data/features/labels/task_a_query_feature_labels')
import numpy as np
from file_utils import load_object
from graph_utils import get_average_distance_from_node_to_nodes
import pandas as pd
subreddits_dictionary = load_object(
'data/dictionaries/subreddits_dictionary_reduced')
all_subreddits = list(subreddits_dictionary.keys())
users_dictionary = load_object('data/dictionaries/users_dictionary_reduced')
all_users = list(users_dictionary.keys())
involvement_dictionary = load_object(
'data/dictionaries/involvement_dictionary_reduced')
sus_node_features = load_object("data/features/SuS_node_features")
usu_node_features = load_object("data/features/UsU_node_features")
sus_hop_count_matrix = load_object("data/distances/SuS_hop_count")
sus_weighted_distance_matrix = load_object("data/distances/SuS_weighted")
usu_hop_count_matrix = load_object("data/distances/UsU_hop_count")
usu_weighted_distance_matrix = load_object("data/distances/UsU_weighted")
sus_community_membership = load_object(
"data/features/SuS_community_membership")
usu_community_membership = load_object(
"data/features/UsU_community_membership")
sus_node_embeddings = load_object("data/features/SuS_node_embeddings")
usu_node_embeddings = load_object("data/features/UsU_node_embeddings")
subreddits_keyword_embeddings = load_object(
"data/features/subreddits_keyword_embeddings")
users_keyword_embeddings = load_object(
"data/features/users_keyword_embeddings")
datasets.append(dataset_without_columns)
dataset_without_columns = dataset.drop(extra_columns, axis=1)
datasets.append(dataset_without_columns)
return datasets
if __name__ == "__main__":
if not isfile('scores/ablation/task_a_comment_scores.tsv'):
print("Performing ablation experiment for comment model...")
comment_dataset = pd.read_csv(
'data/datasets/task_a_comment_dataset.tsv',
sep='\t',
index_col=False,
header=0,
encoding='utf-8')
comment_class_labels = load_object(
'data/class_labels/task_a_comment_class_labels')
comment_ablation_scores = {}
ablation_datasets = get_ablation_datasets(comment_dataset)
del comment_dataset
for ablation_label, ablation_dataset in zip(ablation_labels,
ablation_datasets):
_, scores = train_model(ablation_dataset,
comment_class_labels,
optimize_parameters=False)
comment_ablation_scores[ablation_label] = scores['Ensemble model']
comment_ablation_scores = pd.DataFrame(np.array(list(
comment_ablation_scores.values()),
dtype='float'),
index=ablation_labels,